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The Journal of Supercomputing

Erschienen in:

16.01.2018

WCP-RNN: a novel RNN-based approach for Bio-NER in Chinese EMRs

Paper ID: FC_17_25

verfasst von: Jianqiang Li, Shenhe Zhao, Jijiang Yang, Zhisheng Huang, Bo Liu, Shi Chen, Hui Pan, Qing Wang

Erschienen in: The Journal of Supercomputing | Ausgabe 3/2020

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Abstract

Deep learning has achieved remarkable success in a wide range of domains. However, it has not been comprehensively evaluated as a solution for the task of Chinese biomedical named entity recognition (Bio-NER). The traditional deep-learning approach for the Bio-NER task is usually based on the structure of recurrent neural networks (RNN) and only takes word embeddings into consideration, ignoring the value of character-level embeddings to encode the morphological and shape information. We propose an RNN-based approach, WCP-RNN, for the Chinese Bio-NER problem. Our method combines word embeddings and character embeddings to capture orthographic and lexicosemantic features. In addition, POS tags are involved as a priori word information to improve the final performance. The experimental results show our proposed approach outperforms the baseline method; the highest F-scores for subject and lesion detection tasks reach 90.36 and 90.48% with an increase of 3.10 and 2.60% compared with the baseline methods, respectively.

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Yang J-J, Li J, Mulder J, Wang Y, Chen S, Wu H, Wang Q, Pan H (2015) Emerging information technologies for enhanced healthcare. Comput Ind 69:3–11CrossRef

Zhang S, Elhadad N (2013) Unsupervised biomedical named entity recognition: experiments with clinical and biological texts. J Biomed Inform 46(6):1088–1098CrossRef

Mao R, Xu H, Wu W, Li J, Li Y, Lu M (2015) Overcoming the challenge of variety: big data abstraction, the next evolution of data management for AAL communication systems. IEEE Commun Mag 53(1):42–47CrossRef

Lafferty J, McCallum A, Pereira FCN (2001) Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Enabling recognition of diseases in biomedical text with machine learning: corpus and benchmark

Tsochantaridis I, Joachims T, Hofmann T, Altun Y (2005) Large margin methods for structured and interdependent output variables. J Mach Learn Res 6(Sep):1453–1484MathSciNetMATH

Mao R, Zhang P, Li X, Liu X, Lu M (2016) Pivot selection for metric-space indexing. Int J Mach Learn Cybern 7(2):311–323CrossRef

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444CrossRef

Tompson JJ, Jain A, LeCun Y, Bregler C (2014) Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in neural information processing systems. pp 1799–1807

Collobert R, Weston J, Bottou L, Karlen M, Kavukcuoglu K, Kuksa P (2011) Natural language processing (almost) from scratch. J Mach Learn Res 12(Aug):2493–2537MATH

10.

Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems. pp 3111–3119

11.

Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng A, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. pp 1631–1642

12.

Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078

13.

Seok M, Song H-J, Park C-Y, Kim J-D, Kim Y-S (2016) Named entity recognition using word embedding as a feature. Int J Softw Eng Appl 10(2):93–104

14.

Sahu SK, Anand A (2016) Recurrent neural network models for disease name recognition using domain invariant features. arXiv preprint arXiv:1606.09371

15.

Tang B, Cao H, Wang X, Chen Q, Xu H (2014) Evaluating word representation features in biomedical named entity recognition tasks. BioMed Res Int 2014:240403

16.

Li C, Song R, Liakata M, Vlachos A, Seneff S, Zhang X (2015) Using word embedding for bio-event extraction. In: Proceedings of the 2015 Workshop on Biomedical Natural Language Processing (BioNLP 2015). Association for Computational Linguistics, Stroudsburg, pp 121–126

17.

Nie Y, Rong W, Zhang Y, Ouyang Y, Xiong Z (2015) Embedding assisted prediction architecture for event trigger identification. J Bioinform Comput Biol 13(03):1541001CrossRef

18.

Jagannatha AN, Yu H (2016) Bidirectional rnn for medical event detection in electronic health records. In: Proceedings of the Conference. Association for Computational Linguistics. North American Chapter. Meeting, 2016, 473. NIH Public Access

19.

Jagannatha AN, Yu H (2016) Structured prediction models for rnn based sequence labeling in clinical text. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, 2016, 856. NIH Public Access

20.

Lei J, Tang B, Lu X, Gao K, Jiang M, Xu H (2013) A comprehensive study of named entity recognition in chinese clinical text. J Am Med Inform Assoc 21(5):808–814CrossRef

21.

Yan Y, Wen D, Wang Y, Wang K (2014) Named entity recognition in chinese medical records based on cascaded conditional random field. J Jilin Univers Eng Technol Edn 6:048

22.

Dong X, Qian L, Guan Y, Huang L, Yu Q, Yang J (2016) A multiclass classification method based on deep learning for named entity recognition in electronic medical records. In: Scientific data summit (NYSDS). IEEE, New York, pp 1–10

23.

Wu Y, Jiang M, Lei J, Xu H (2015) Named entity recognition in chinese clinical text using deep neural network. Stud Health Technol Inform 216:624

24.

Botha J, Blunsom P (2014) Compositional morphology for word representations and language modelling. In: International Conference on Machine Learning. pp 1899–1907

25.

Chen X, Xu L, Liu Z, Sun M, Luan H-B (2015) Joint learning of character and word embeddings. In: IJCAI. pp 1236–1242

26.

Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166CrossRef

27.

Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRef

28.

Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5):602–610CrossRef

29.

Dyer C, Ballesteros M, Ling W, Matthews A, Smith NA (2015) Transition-based dependency parsing with stack long short-term memory. arXiv preprint arXiv:1505.08075

30.

Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259

31.

Kinga D, Adam JB (2015) A method for stochastic optimization. In: International Conference on Learning Representations (ICLR)

Titel: WCP-RNN: a novel RNN-based approach for Bio-NER in Chinese EMRs
Paper ID: FC_17_25
verfasst von: Jianqiang Li
Shenhe Zhao
Jijiang Yang
Zhisheng Huang
Bo Liu
Shi Chen
Hui Pan
Qing Wang
Publikationsdatum: 16.01.2018
Verlag: Springer US
Erschienen in: The Journal of Supercomputing / Ausgabe 3/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI: https://doi.org/10.1007/s11227-017-2229-x

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